Few Shot Learning Framework to Reduce Inter-observer Variability in
Medical Images
- URL: http://arxiv.org/abs/2008.02952v1
- Date: Fri, 7 Aug 2020 02:05:51 GMT
- Title: Few Shot Learning Framework to Reduce Inter-observer Variability in
Medical Images
- Authors: Sohini Roychowdhury
- Abstract summary: Quality assuring large volumes of annotated medical image data can be subjective and expensive.
We present a novel standardization framework that implements three few-shot learning (FSL) models.
We also propose a novel target label selection algorithm (TLSA) that measures relative agreeability between RPs and the manually annotated target labels.
- Score: 1.2335698325757494
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Most computer aided pathology detection systems rely on large volumes of
quality annotated data to aid diagnostics and follow up procedures. However,
quality assuring large volumes of annotated medical image data can be
subjective and expensive. In this work we present a novel standardization
framework that implements three few-shot learning (FSL) models that can be
iteratively trained by atmost 5 images per 3D stack to generate multiple
regional proposals (RPs) per test image. These FSL models include a novel
parallel echo state network (ParESN) framework and an augmented U-net model.
Additionally, we propose a novel target label selection algorithm (TLSA) that
measures relative agreeability between RPs and the manually annotated target
labels to detect the "best" quality annotation per image. Using the FSL models,
our system achieves 0.28-0.64 Dice coefficient across vendor image stacks for
intra-retinal cyst segmentation. Additionally, the TLSA is capable of
automatically classifying high quality target labels from their noisy
counterparts for 60-97% of the images while ensuring manual supervision on
remaining images. Also, the proposed framework with ParESN model minimizes
manual annotation checking to 12-28% of the total number of images. The TLSA
metrics further provide confidence scores for the automated annotation quality
assurance. Thus, the proposed framework is flexible to extensions for quality
image annotation curation of other image stacks as well.
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